Data Science Experience
Boeing, Seattle, WA, January 2016 - Present
- Team lead for project to model failures in airplane engines using general additive models to identify operational behaviors and environmental conditions associated to the failures. Model results were used to provide airline specific recommendations for modifying take off and climb procedures to extend engine life.
- Developed two predictive maintenance alerts and two diagnostic alerts using multiple sources of aircraft sensor data. These alerts have led to reductions in unscheduled maintenance and part replacements saving more than half a million dollars in 2018.
- Contributed to building the computing system, processes and tools used by the alerting and prognostic team to deploy more than 10 predictive maintenance alerts to multiple airline customers in less than six months.
- Developed a system utilizing R and R Markdown to automate the delivery of custom aircraft status reports to airline maintenance crews at the arrival airport within one hour of departure, reducing time between flights.
- Collaborated with an engineering team to build a dashboard suite for evaluating aircraft part reliability over time, eliminating reliance on external teams and suppliers by integrating and visualizing data from four databases.
- Built and maintain a data extraction pipeline for proprietary XML documents that are the primary source for a tool allowing airlines to evaluate 12000 available aircraft modifications.
Airlines Reporting Corporation, Arlington, VA, May 2013 - January 2016
- Designed and carried out research projects on a variety of topics relevant to the airline industry that gained widespread media coverage. Notable reports include a 2014 covered by the Seattle Times and ABC News.
- Collaborated with Expedia to provide analysis featured in Travel Check-Up: Air Travel Trends 2015 and Preparing for Take-Off: Air Travel Outlook for 2016.
- Built a dashboard suite to assist destination-marketing clients in making business decisions.
- Developed predictive models to inform staffing decisions at airport retail locations.
- Used topic modeling and sentiment analysis to automate the evaluation of customer comments in an annual satisfaction survey.
Data Kind DC, Washington, DC 2015
- Participated in development of interactive of labor violations for the Fair Labor Association
- Collaborated with other data scientists using different programming languages and with different areas of expertise
- Contributed time and skills to data projects for local non-profits
- Warlick, S. noaaoceans: Collect Ocean Data From NOAA. R package version 0.1.0. Available on Github and CRAN
“Enabling Rapid Diagnosis & Prescriptive Maintenance.” Presentation, Boeing Technical Excellence Conference, St. Charles, MO, May 2018
“Applying Natural Language Processing and Text Mining Techniques to Marine Mammal Literature: Identifying and Summarizing Future Research Directions.” Speed talk, 22nd Biennial Conference on the Biology Of Marine Mammals, Halifax, NS, Oct 2017
“Maps Without the Hassle Using ggmap.” Statistical Programming DC, Washington, DC, March 2015
- Betz, F., Coleman, R., Jackson, G., Nakhjavani, O., Puigh, D., Runo, S., Schimert, J., Warlick, S., Zaikin, A. Diagnostic System For A Closed Fluid System. U.S. Patent Application 15\833564. Filed January 2018. Patent Pending.
American University, Washington, DC, 2014
- Master of Science: Statistics
Grinnell College, Grinnell, IA, 2008
- Bachelor of Arts: History
Skills & Software
- Experienced in the use of R, Python, Tableau, SAS, and SPSS for statistical analysis and data visualization.
- Daily use of SQL for report generation and data manipulation in relational databases.
- Experienced with Git, GitHub, and GitLab for version control and collaboration.
- Experienced using Travis CI and Codcov for software development.
- Familiar with Hadoop and Spark platforms for data aggregation and analysis.
- Familiar with Digital Ocean and Azure cloud computing platforms.